55 citations · 171 across the 21 of their papers we have counts for
7 papers · 1 filter
RLTF: Reinforcement Learning from Unit Test Feedback
Jiate Liu, Yiqin Zhu, Kaiwen Xiao +4
The goal of program synthesis, or code generation, is to generate executable code based on given descriptions. Recently, there has been an increasing number of studies employing re…
Policy Space Diversity for Non-Transitive Games
Jian Yao, Weiming Liu, Haobo Fu +4
Policy-Space Response Oracles (PSRO) is an influential algorithm framework for approximating a Nash Equilibrium (NE) in multi-agent non-transitive games. Many previous studies have…
Maximum Entropy Heterogeneous-Agent Reinforcement Learning
Jiarong Liu, Yifan Zhong, Siyi Hu +4
Multi-agent reinforcement learning (MARL) has been shown effective for cooperative games in recent years. However, existing state-of-the-art methods face challenges related to samp…
Future-conditioned Unsupervised Pretraining for Decision Transformer
Zhihui Xie, Zichuan Lin, Deheng Ye +3
Recent research in offline reinforcement learning (RL) has demonstrated that return-conditioned supervised learning is a powerful paradigm for decision-making problems. While promi…
Towards Effective and Interpretable Human-Agent Collaboration in MOBA Games: A Communication Perspective
Yiming Gao, Feiyu Liu, Liang Wang +11
MOBA games, e.g., Dota2 and Honor of Kings, have been actively used as the testbed for the recent AI research on games, and various AI systems have been developed at the human leve…
Revisiting Estimation Bias in Policy Gradients for Deep Reinforcement Learning
Haoxuan Pan, Deheng Ye, Xiaoming Duan +4
We revisit the estimation bias in policy gradients for the discounted episodic Markov decision process (MDP) from Deep Reinforcement Learning (DRL) perspective. The objective is fo…
Sample Dropout: A Simple yet Effective Variance Reduction Technique in Deep Policy Optimization
Zichuan Lin, Xiapeng Wu, Mingfei Sun +4
Recent success in Deep Reinforcement Learning (DRL) methods has shown that policy optimization with respect to an off-policy distribution via importance sampling is effective for s…